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检索条件"主题词=binary Particle Swarm Optimization"

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Intelligent Scheduling of Heat Pump to Minimize the Cost of Electricity 21

Intelligent Scheduling of Heat Pump to Minimize the Cost of ...

引用

21st International Scientific Conference on Electric Power Engineering (EPE)

作者： Lashkari, Behzad Chen, Yuxiang Musilek, Michal Musilek, Petr Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 1H9 Canada Univ Hradec Kralove Dept Appl Cybernet Hradec Kralove Czech Republic

ISBN: (纸本)9781728194790

Space heating is responsible for a significant portion of energy consumption in the residential sector. As such, space heating has a great potential for energy savings. The heat pump is an important energy conservation option that allows modification of residential energy demand profile and subsequent reduction of electricity consumption and costs. Living comfort of the residents is the other side of the coin that also must be considered during the heating optimization process. This article presents an intelligent approach to heat pump scheduling problem based on metaheuristic optimization algorithms. In particular, we consider mutation-based binary particle swarm optimization and genetic algorithm. Simulation results confirm that the proposed approach can optimize the heat pump scheduling task without sacrificing the thermal comfort of residents.

关键词： home energy management heat pump scheduling binary particle swarm optimization genetic algorithm demand side management

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Entropy Based Occlusion Removal with DTCWT and DCT as Feature Extractor for Iris Recognition 6

Entropy Based Occlusion Removal with DTCWT and DCT as Featur...

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2015 IEEE International Conference on Computational Intelligence and Research (ICCIC)

作者： Likitha, L. Gupta, Diksha Manikantan, K. MS Ramaiah Inst Tech Dept Elect & Commun Engn Bangalore 560054 Karnataka India

ISBN: (纸本)9781479978496

The iris pattern of human eye is considered to be unique and distinct that it plays an important role as reliable biometric system for authentication purposes. Iris recognition has become a challenging issue under varying lighting or contrast conditions and due to occlusions such as eyelashes. Therefore, in this paper we have proposed a novel technique for eliminating the eyelashes, namely Entropy based Occlusion Removal which is applied after iris localization. Also a feature extractor based on Dual Tree Complex Wavelet Transform (DTCWT) is used for extracting shift-invariant features in combination with Discrete Cosine Transform (DCT). binary particle swarm optimization (BPSO) is used for feature selection that selects the optimum features extracted by DTCWT+DCT. The Euclidean distance classifier estimates the similarity between the testing and training images. The experiments conducted on IITD and CASIA iris databases depict promising performance of the proposed technique.

关键词： Iris recognition Entropy Dual Tree Complex Wavelet Transform binary particle swarm optimization Discrete Cosine Transform

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DWT-based Face Recognition using Fast Walsh Hadamard Transform and Chiral Image Superimposition as pre-processing techniques 2

DWT-based Face Recognition using Fast Walsh Hadamard Transfo...

引用

2nd International Conference on Electronics and Communication Systems (ICECS)

作者： Niveditha, G., V Sharmila, B. P. Manikantan, K. Ramachandran, S. MS Ramaiah Inst Tech Dept Elect & Commun Engn Bangalore 560054 Karnataka India SJB Inst Tech Dept Elect & Commun Engn Bangalore 560060 Karnataka India

ISBN: (纸本)9781479972258

One of the major challenges encountered by current Face Recognition (FR) techniques lies in the difficulties of handling varying poses and illuminations. In this paper we propose three novel techniques, viz., Fast Walsh Hadamard Transform (FWHT), Chiral Image Superimposition (CIS) and Discrete Wavelet Transform (DWT), to improve the performance of a FR system. FWHT is used for illumination normalization, CIS combats pose variances and DWT along with its sub-bands LL and LH are used for efficient feature extraction. A binary particle swarm optimization (BPSO) based feature selection is used to reduce the number of selected facial features for recognition. Individual stages of the FR system are examined and an attempt is made to improve each stage. Experimental results show the promising performance of the proposed techniques for face recognition on four benchmark face databases, namely, Color FERET, ORL, CMU PIE and Extended Yale B.

关键词： face recognition feature extraction image preprocessing binary particle swarm optimization

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Profit-Aware Resource Allocation for 5G Sliced Networks

Profit-Aware Resource Allocation for 5G Sliced Networks

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European Conference on Networks and Communications (EuCNC)

作者： Oladejo, Sunday O. Falowo, Olabisi E. Univ Cape Town Dept Elect Engn Sch Engn & Built Environm Cape Town South Africa

ISBN: (数字)9781538614785

ISBN: (纸本)9781538614785

In this paper, we investigate the radio resource allocation for the 5G Sliced Network from the perspective of the network profit. Based on the quality of service requirements of the different network slices case, a Profit-Aware Resource Allocation (PARAA) is proposed. The Power-Bandwidth rule is employed in the cost estimation, while the optimization problem is formulated as a Mixed Integer Non-Linear Problem (MINLP) and the binary particle swarm optimization (BIPSO) is used to solve the profit resource allocation problem. In respect to this, the performance of the algorithm is investigated via intensive Monte-Carlo based simulations taking into consideration the effect of coverage radius, number of users per slice and the allocated transmit power of each user.

关键词： 5G Network Slices binary particle swarm optimization Network Profit Monte-Carlo

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Improving Representation in Benchmarking of binary PSO Algorithms 31th

Improving Representation in Benchmarking of Binary PSO Algor...

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31st Australasian Joint Conference on Artificial Intelligence (AI)

作者： Yamada, Shinichi Neshatian, Kourosh Univ Canterbury Dept Comp Sci & Software Engn Christchurch New Zealand

ISBN: (纸本)9783030039912;9783030039905

binary PSO algorithms are extensions of the PSO algorithm that enjoy some of the social intelligence properties of the original algorithm. The intensive local search ability is one of the most important characteristics of PSO. In this paper, we argue that, when evaluating binary PSO algorithms against common real-value benchmark problems-a common practice in the literature-the representation of the search space can have a significant effect on the results. For this purpose we propose the use of reflected binary code, which is a minimal change ordering representation for mapping a binary genotype space to a real phenotype space, while preserving the notion of locality in the phenotype space.

关键词： binary particle swarm optimization Reflected binary code Minimal change ordering Representation Benchmarking

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Features Selection Approaches for Intrusion Detection Systems based on Evolutionary Algorithms

Features Selection Approaches for Intrusion Detection System...

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3rd International Conference on Signals, Circuits and Systems

作者： Zaman, Safaa El-Abd, Mohammed Karray, Falchri Univ Waterloo ECE Dept Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424443970

Intrusion Detection Systems (IDSs) deal with large amount of data containing irrelevant and redundant features, which leads to slow training and testing processes, heavy computational resources and low detection accuracy. Therefore, the features selection is an important issue in intrusion detection. Reducing the features set improves the system accuracy and speeds up the training and testing phases considerably. In this paper, we investigate the use of evolution algorithms for features selection approach in Intrusion Detection System (IDS). In order to this we have implemented three different evolutionary computation algorithms: binary Genetic Algorithms (GAs), binary particle swarm optimization (PSO) and Differential Evolution (DE). The performances of three algorithms according to different metrics are compared when applied to the KDD Cup 1999 dataset.

关键词： Intrusion detection Systems features selection Evolution Algorithms binary Genetic Algorithms binary particle swarm optimization Differential Evolution

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Face Recognition using Homomorphic Filtering as a Pre-processing Technique

Face Recognition using Homomorphic Filtering as a Pre-proces...

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International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications (C2SPCA)

作者： D'Cunha, Nihal Winston Birajdhar, Sachin A. Manikantan, K. Ramachandran, S. MS Ramaiah Inst Tech Dept Elect & Commun Engg Bangalore 560054 Karnataka India SJB Inst Tech Dept Elect & Commun Engg Bangalore 560060 Karnataka India

ISBN: (纸本)9781479910854

The appearance of the face will vary drastically when illumination, pose and expression change. Variations in these conditions make Face Recognition (FR) an even more challenging and difficult task. In this paper, we propose three novel techniques, viz., Homomorphic Filtering (HF), Image Flip (IF) and Raster Scan Mapping (RSM), to improve the performance of a FR system. HF is a pre-processing technique used to normalize the illumination variations. IF is used to neutralize pose and expression variations. RSM is used to map 2D to 1D, to increase the ability to store the intensity value of all the spotted lines. DWT and DCT are used for efficient feature extraction and a binary particle swarm optimization based feature selection algorithm is used to search the feature space for the optimal feature subset. Experimental results show the promising performance of the proposed techniques for FR on four benchmark face databases, namely, CMUPIE, Color FERET, UMIST and ORL databases.

关键词： Face Recognition Feature Extraction Image pre-processing Homomorphic Filtering binary particle swarm optimization

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Face Recognition using Gabor Filter based Feature Extraction with Anisotropic Diffusion as a pre-processing technique 1

Face Recognition using Gabor Filter based Feature Extraction...

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International Conference on Advanced Computing Technologies and Applications (ICACTA)

作者： Abhishree, T. M. Latha, J. Manikantan, K. Ramachandran, S. MS Ramaiah Inst Technol Dept Elect & Commun Engn Bangalore 560054 Karnataka India SJB Inst Technol Dept Elect & Commun Engn Bangalore 560060 Karnataka India

Face Recognition (FR) across pose, illumination and expression is a problem of fundamental importance in computer vision. In this paper, we propose two novel techniques, viz., Anisotropic Diffusion (AD) based pre-processing and Gabor filter based Feature Extraction, to improve the performance of a FR system. AD preserves the edges leading to facial image smoothing and enhancement. Gabor filter is used to capture facial features aligned at specific angles. Along with these, a binary particle swarm optimization based feature selection algorithm is used to search the feature space for the optimal feature subset. Individual stages of the FR system are examined and an attempt is made to improve each stage. The proposed approach has been tested on four benchmark face databases, namely, ORL, Color FERET, Cropped Yale B and FEI datasets, and demonstrates superior performance compared to existing methods in the presence of pose, illumination and expression variations. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Face Recognition Feature Extraction Image Pre-processing binary particle swarm optimization Anisotropic Diffusion Gabor filter

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Face Localization using Skin colour and Maximal Entropy based particle swarm optimization for Facial Recognition 4

Face Localization using Skin colour and Maximal Entropy base...

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4th IEEE-Uttar-Pradesh-Section International Conference on Electrical, Computer and Electronics (UPCON)

作者： Jois, Subramanya Ramesh, Rakshith Kulkarni, Anoop C. Ramaiah Inst Technol Bangalore Karnataka India Indian Inst Sci Robrt Bosch Ctr Cyber Phys Syst Bangalore Karnataka India Indian Inst Sci Dept Elect Commun Engn Bangalore Karnataka India

ISBN: (纸本)9781538630044

One amongst the vital pre-processing operation in every Face Recognition System is Face localization. There is a growing need for algorithms that quickly yield the correct region of interest around the face. Here an algorithm is proposed which performs skin detection using particle swarm optimization for face localization. Where minimization of fitness function is used to resolve optimization problems. Here bounding boxes are modelled as particles, or randomly distributed test vectors to ascertain for the region of interest in an image where it is likely that the face exists, with the fitness value returned by the fitness function that tests the interior of a box for maximum entropy and maximum skin like features, which in combination indicates how likely the interior represents a face. Consequently, the particle (bounding box) with the best fitness value is chosen as face region of the image, and the output is fed to a binary particle swarm optimization based Face Recognition System therefore yielding higher recognition rates than conventional systems.

关键词： binary particle swarm optimization swarm intelligence Skin Detection Face localization Face Recognition

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Generation Cost and Loss Power Minimization in Intentional Islanded Networks Based on BPSO 6

Generation Cost and Loss Power Minimization in Intentional I...

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6th IEEE Power India International Conference (PIICON)

作者： Wongwantanee, Nachapol Singh, Jai Govind Rajpurohit, Bharat Singh Asian Inst Technol Sch Environm Resources & Dev Energy Field Study Pathum Thani 12120 Thailand Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Predes India

ISBN: (纸本)9781479960422

At present, large electrical undergoes stability problems due to unexpected events such as storms, earthquakes, acts of terrorism etc. Power system operation may close to its stability limits, and power quality will be under standard limits. The large expansion of renewable energy generation has transformed the ordinary electric power systems become a more complicated distributed system. Use of intentional islanding has many benefits for separating one huge network to many islanded network for sake of good stability and quality of electricity. One major benefit is to evade and block wide electricity blackout form spreading of cascading failure. For operating islanded networks, each network has to control its power balance and stability for continuation of power supply with optimum operating point. The purposes of this paper are to optimize generation cost and loss power, and to find the suitable form of islanded networks by using binary particle swarm optimization together with network reconfiguration in Matlab and Matpower scripting.

关键词： Intentional Iskmding Operation Isltutded Network binary particle swarm optimization Loss Power Minimization Generation Cost Minimization

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